多无人机航迹规划的自适应B样条算法

无人机集群能更高效地完成复杂和具有挑战性的任务，航迹规划和编队控制是无人机集群的研究重点。针对复杂环境下的无人机编队控制问题，提出了一种结合分段自适应B样条（Piecewise Adaptive B-Spline，PABS）方法的领航-跟随策略。采用滚动时域控制及快速粒子群优化算法为领航者无人机生成一条安全的参考航迹，并根据跟随者与领航者保持的几何关系为跟随者无人机生成参考航迹。针对生成的跟随者航迹不平滑以及可能与障碍物发生碰撞的问题，使用PABS方法对跟随者航迹进行平滑和避障处理。实验表明，使用滚动时域控制及快速粒子群优化算法及PABS方法能为领航-跟随策略下的无人机编队生成安全平滑的航迹，相比于圆弧插补技术，PABS方法能使航迹更光滑。

Adaptive B-Spline Algorithm for Multi-UAV Path Planning

UAV clusters can perform complex and challenging tasks more efficiently, and path planning and formation control are the focus of research on UAV clusters. Aiming at the problem of UAV formation control in complex environments, a leader-follower strategy combining Piecewise Adaptive B-Spline(PABS)is proposed. The Receding Horizon Control and the Fast Particle Swarm Optimization(RHC-FPSO)algorithm is used to generate a safe reference path for the leader. And the reference paths are generated for the followers according to the geometric relationship maintained by the followers and the leader. Followers’ paths are smoothed by the PABS method for the problem that the paths of followers may not be smooth. Since the path will collide with obstacles in the environment, the path is planned by the adaptive B-spline method to achieve obstacle avoidance. Experiments show that the RHC-FPSO method and the PABS method can generate safe and smooth paths for the UAVs in the formation under the leader-follower strategy. Compared with the circular interpolation technique, the PABS method can make the path smoother.